Optimizing research with GPUs on Hoffman2
🎯 Workshop Overview
🚀 Discover the power of GPU computing to accelerate your research on UCLA’s Hoffman2 cluster! This beginner-friendly workshop will guide you through the basics of GPU utilization, enhancing your projects with cutting-edge computational efficiency. ⭐
👉 What you’ll learn:
- 🧠 Understanding GPU architecture and its benefits
- 💻 Hands-on access to Hoffman2’s advanced GPU resources
- 🐍 Utilizing Python and R for GPU computing
- RPyLab - A container with RStudio and Jupyter with GPU support (experimental)
For suggestions: cpeterson@oarc.ucla.edu
🤔 What are Graphic Processing Units?
- Initially developed for processing graphics and visual operations
- CPUs were too slow for these tasks
- Architecture of GPUs allows to handle massively parallel tasks efficiently
- Found in everything from PCs, mobile phones, gaming consoles, and more
🚀 In the mid-2000s, GPUs began to be used for non-graphical computations. NVIDIA introduced CUDA, a programming language that allows for compiling non-graphic programs on GPUs, spearheading the era of General-Purpose GPU (GPGPU).
GeForce 256
- First ‘GPU’ in 1999
- 32 MB of memory
- 960 MFLOPS (FP32)
A100
- 80 GB of memory
- 19.5 TFLOPS (FP32)
🌐 Applications of GPUs
GPUs are ubiquitous and found in devices ranging from PCs to mobile phones, and gaming consoles like Xbox and PlayStation.
Though initially designed for graphics, GPUs are now used in a wide range of applications.
- 🧠 Machine Learning: Training and inference especially in Deep Learning neural networks
- 📖 Large Language Models: Training for NLP models
- 🔍 Data Science: Accelerating data processing and analysis
- 💻 High-Performance Computing: Simulations and scientific computing
🚄 GPU Performance
⚡ The Power of GPUs
The significant speedup offered by GPUs comes from their ability to parallelize operations over thousands of cores, unlike traditional CPUs.
🔧 GPU Workflow
🤹♂️ GPU considerations
- 🚧 Code Optimization: Some codes are not suitable for GPU.
- 👷 GPU architecture: Some codes can run more efficiently on some GPUs over others, or sometimes not at all.
- 🔄Overhead: Data transfer between CPU and GPU can be costly.
- 🧠 Memory Management: GPU memory is limited and can be a bottleneck.
🧩 CUDA
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model from NVIDIA. It enables developers to write software that harnesses the power of GPUs for more than just graphics — expanding into high-performance computing and deep learning.
On Hoffman2, you can compile CUDA code by loading the cuda module. This prepares your environment with tools from the CUDA toolkit, which includes essential libraries and compilers for GPU code execution.
📚 CUDA libraries
⏳ CUDA code example
👗 Fashion MNIST
Explore machine learning with the “Fashion MNIST” dataset using TensorFlow:
Approach:
- We will use TensorFlow to train a Netural Net model for predicting fashion categories.
Dataset Overview:
- 📸 Images: 28x28 grayscale images of fashion products.
- 📊 Categories: 10, with 7,000 images per category.
- 🧮 Total Images: 70,000.
🧬 DNA classification with PyTorch
DNA Sequence Classification with PyTorch
- 🎯 Objective: Develop a model to classify DNA sequences.
- 🔬 Gene Regions: Segments of DNA containing codes for protein production.
- 🧪 Dataset Creation: Generate random DNA sequences labeled as ‘gene’ or ‘non-gene’.
- 🤖 Model Development: Use PyTorch to build a model predicting the presence of ‘gene’ regions.
- 🚀 Leveraging GPUs: Utilize the parallel processing power of GPUs for efficient training.
🧬 Rapids for Genomic Data Analysis
We will use RAPIDS for genomic data analysis. RAPIDS is a popular platform to run data workflows, tasks, and manipulations, as well as, machine learning on GPUs.
We will
- Applying conditions to filter dataframes based on depth, quality, and allele frequency.
- Grouping data by chromosome and calculating mean statistics for depth, quality, and allele frequency.
- Speed comparison of these operations on GPU versus CPU.
💧 H2O.ai ML Example
Explore machine learning with H2O.ai using the Combined Cycle Power Plant dataset:
- H2O.ai is an open-source platform for machine learning and AI.
- We will work through an example from H2o-tutorials.
- ⭐ Objective: Predict the energy output of a Power Plant using temperature, pressure, humidity, and exhaust vacuum values.
- This example, we will use the R API, but H2O.ai has a Python API as well
- We will use XGBoost, a popular gradient boosting algorithm, to train the model.
